Exploring Factors that Contribute to Country Development

“The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.” -UN Development Program

In the world map below, countries are colored according to their Human Development Index score. Each country is assigned an HDI score - a number between 0 and 1, designed, in a rough sense, to measure quality of life. Notice that countries further from the equator are more likely to have a high HDI score than countries closer to the equator. This trend shows up as a visual gradient on the map: the further from the equator, the higher the HDI score, the more blue the countries appear. But this is not a general rule. The term “Gloabl South” is often used to describe a collection of so-called “under-developed” countries near the equator and south of it, a collection which the map below suggests.

However, this map is quite one dimensional. Just what exactly does HDI tell us? What, in concrete terms, does “human development” mean? The goal of the following analysis is to shed light on HDI and its limitations through other measures, in particular measures related to expected years of education, literacy rates, population density, and women’s empowerment.

Education

HDI vs. Expected Years of Education

Education is widely considered to have a strong contribution to the advancements of societies and the quality of life of the people who live in them. In the definition of the HDI measurement mentioned above, being knowledgeable is a contributing factor to a country’s HDI value. Curious to see how the length of time spent in school affects the UN’s measure of knowledge and a country’s HDI, we plotted countries’ HDI vs expected years of schooling to observe the trend between the two. Note: The points that belong to the NA continent group are countries that were unsuccessfully matched with a continent during the data wrangling process.

The linear regression of the plotted points has an \(R^2\) of 0.802, supporting the assumption of a strong correlation between the two variables. However, there are clearly some other factors that must influence the HDI, as we can see that Europe has more countries plotted over the regression while Africa has more countries plotted below the regression. Although we know the main factors that are used to measure HDI, these results point towards regional variables influencing the HDI (could be issues accessing resources, emigration, etc.). In the future, we’d be curious to explore the common issues among the countries below the regression and common accomplishments among countries above the regression.

Chloropleth Comparison

Below, we’ve included chloropleths of the expected years of education and HDI so they can be compared side by side. These chloropleths help the countries that are plotted further from the regression stand out because of their more drastic changes in shade from one graph to the other compared to some of the other countries’ more nuanced changes.

Education

HDI

Literacy Rate and HDI

We continue our inquiry into HDI and education by asking: Which is a better predictor of literacy rates - HDI, or average number of years of education? Moreover, what does it mean if HDI predicts literacy rates better than average number of years of education?

For each country, we can find an expected number of years of schooling: this is the number of years the average student attends school. In countries where the average years of schooling is higher, we expect to find higher average literacy rates.

For each continent, we calculated two correlation coefficients. First, we found the correlation between HDI score and literacy rate; in other words, how well does HDI predict literacy rate for that continent. Second, we found the correlation between average years of education and literacy rate; in other words, how well does years of schooling predict literacy rate for that continent.

Next, for each continent, we found the difference between these two correlations. The interesting results are those where this difference is small. A small difference in these two values means that “development” is as good a predictor of literacy rates as years of education. A small difference indicates that non-educational “developmental” factors are influencing literacy rates.

Observe that two continents, South America and Africa, are picked out as having a smaller difference. This means that in these two continents, extra-educational factors are influencing literacy rates. This observation tracks with the delineation into “Global South” and “Global North” indicated by the plot of HDI. That is, the literacy rates of South America and Africa, continents situated in the Global South, suffer from extra-educational factors.

One problem with this analysis is that it is not granular. It gives us a view of the world that is split into seven, when in reality, the world has far more than seven borders.

Our next analysis clusters countries according to literacy rate and population density. The goal of the analysis is to show that the division into Global North and Global South is inadequate to understand differences in literacy rates. In other words, the delineation into North and South indicated by HDI is a simplification - the actual situation is more complicated.

Before this analysis can proceed, we first make an observation about the relationship between population density and literacy rates. Compare the plots of Population Density vs. Literacy Rate, and Log of Population Density vs. Literacy Rate. Observe that a line of best fit on the first plot would be exponential, while in the second, a line of best fit would be linear. This suggests that for the purposes of clustering, it would be appropriate to cluster Log of Population Density against Literacy Rate.

The elbow plot shows that a cluster analysis using three clusters is most appropriate. The plot below associates each country with one of three clusters. The first cluster, 1, consists of countries with high literacy rate and low population density. The second cluster, 2, consists of countries with high literacy rate and high density. The third cluster, 3, consists of countries with low literacy rate. Notice that this third cluster ranges over a wide variety of population densities.

## # A tibble: 3 × 5
##   latestRate_scaled density_scaled  size withinss cluster
##               <dbl>          <dbl> <int>    <dbl> <fct>  
## 1             0.412         -1.07     47     25.9 1      
## 2            -1.77          -0.150    33     42.1 2      
## 3             0.432          0.612    90     54.4 3

Whereas HDI assigns a bare number to each country, the map below expresses relationships between a country’s position, its population density, and its literacy rate. Notice the pockets of contries from the same cluster. Countries from a given cluster tend to be surrounded by others from the same cluster.

The following map colors each country according to its cluster assignment. What is interesting about this map is that it shows how groups of contiguous countries are likely to fall into the same cluster. What does this mean? As an example, examine the pair of North African countries Algeria and Libya. These two countries are near the equator, and in our previous analysis, were part of the group described as the Global South. Here however, we can see that Algeria and Libya belong to a collection of countries with high literacy rate.

Even though, in certain places, HDI is as good a predictor of literacy rate as years of education, this analysis obscures the fact that regional factors are at play. It simply is not the case that a single number - weather HDI or “Expected Years of Education” - can capture the whole situation regarding the literacy of a country. The reason for this is made clear by the map above: the situation regarding literacy in one country does not depend only on that country. Thus, numbers examining countries in isolation are largely incapable of expressing the sitation. The map above, in which pockets of similar countries emarge grouped together geographically, testifies to the reality of this interrelationship. HDI is an excellent tool for examining a country in its isolation. But comprehending the literacy situation in a given country, as the map above demonstrates, requires looking beyond the borders of that country.

Breaking Barriers: Key Factors for Measuring Women’s Progress Across Countries

Why won’t HDI suffice?

Amartya Sen, a Nobel laureate and renowned economist, once said, “empowering women is the key to building the future we want.” This simple yet powerful statement highlights the significance of gender equality and its impact on human development. The notion of human development is rooted in the idea of expanding people’s choices, enabling them to fulfill their potential, and giving them the freedom to lead lives they value. However, the reality is that women’s choices and freedoms are not equal and they continue to be marginalized across the globe.

While countries with higher HDI ranks are generally associated with greater levels of freedom and empowerment, the reality is more complex. For instance, a country’s overall HDI score may mask significant disparities in gender inequality within its population. In many countries, women continue to face discrimination in areas such as education, employment, and political representation, despite their nation’s high HDI ranking. Moreover, the cultural and social norms prevalent in a country can significantly impact the empowerment of women, even in countries with high HDI scores. Therefore, while HDI rankings can provide a broad measure of a country’s level of human development, it is crucial to examine specific indicators that measure the empowerment of women to gain a more nuanced understanding of gender inequality across the globe.

To gain a better understanding of the complex issues that women face worldwide, we will analyze standardized indicators of women’s empowerment across countries based on their population and HDI rank. This analysis will reveal the factors that contribute to gender inequality and highlight areas for improvement to advance women’s empowerment and create a more equitable society.

[Gender Inequality Index Map]( https://v01das-shreya-mathew.shinyapps.io/GenderInequalityIndex/

Indicators of Women’s Empowerment

We will look into four key indicators of Women’s Empowement measure

  1. Adolescent Birth Rate: This metric measures the number of births per 1,000 women between the ages of 15 and 19 in a given year. A high adolescent birth rate is often an indicator of poor sexual and reproductive health outcomes for young women, and can also be a barrier to educational and economic opportunities.

  2. Political Participation: This metric measures the extent to which women are involved in political decision-making processes, including representation in elected offices, participation in political parties, and involvement in civil society organizations. Women’s political participation is important for ensuring that their voices and perspectives are heard in policy-making processes.

  3. Labor Participation: This metric measures the percentage of women who are employed or seeking employment in the labor force. A low labor force participation rate can be an indicator of limited economic opportunities for women, which can in turn contribute to poverty and economic inequality.

  4. Secondary Level Education: The women’s indicator of secondary level education is a metric that measures the percentage of women in a given population who have completed secondary education. This indicator is often used as a measure of women’s educational attainment and their access to educational opportunities.

Heatmap: HDI, Polulation, Key indicators of Women’s Empowerment

This heatmap represents data on different indicators of women’s empowerment across 30 most populous countries, ranked according to their HDI (highest HDI rank in the top and lowest in the bottom). Each row and column of the heatmap represents a different country and a specific indicator, respectively. The colors in the heatmap represent different values of each indicator, with the lighter shades indicating lower values and the darker shades indicating higher values. The values are standardized to make interpretation easier. For Example, darker colors indicate that a particular country is doing better on that indicator compared to countries with lighter colors. Additionally, dendrograms are included at the top and left sides of the heatmap, which show how countries and indicators are clustered together based on similarities in their values.

Key Interpretations

  • The heatmap shows that there is generally a negative correlation between adolescent birthrate and HDI ranking, meaning that countries with higher development tend to have lower adolescent birthrates. However, the heatmap shows that it is not always the case. Some of the exceptions are Uganda and Nigeria, which have high adolescent birthrates despite their moderate development levels. Conversely, some countries with lower HDI rankings, such as India and Algeria, have lower adolescent birthrates

  • The heatmap reveals that women’s political participation is not consistently correlated with a country’s HDI ranking. Contrary to the common belief that higher HDI ranking equates to greater political participation for women, the data shows that this is not always the case. For instance, countries like Mexico, with a lower HDI ranking, exhibit higher women’s political participation compared to Japan, which has a higher HDI ranking but a lower participation rate. This suggests that factors other than development, such as cultural and social norms, may play a role in determining women’s political participation. Therefore, a more nuanced and context-specific approach is necessary to understand the interplay between development and women’s political participation.

  • The heatmap shows a strong positive correlation between women’s labor force participation and HDI ranking. Countries with higher HDI rankings tend to have higher labor force participation rates for women, while those with lower HDI rankings tend to have lower participation rates. However, it is worth noting that there are still significant disparities in women’s labor force participation rates within and across countries, even among those with high HDI rankings.

  • The education level column of the heatmap, specifically reflecting the metric of women’s completion of secondary education, shows a positive correlation with HDI. In general, countries with higher HDI rankings tend to have higher rates of women completing secondary education, indicating greater access to educational opportunities and greater potential for personal and professional growth. However, there are exceptions to this trend. One such exception is Ethiopia, which ranks relatively high in the HDI spectrum but has very low rates of women completing secondary education. This indicates that while Ethiopia has made progress in areas such as healthcare and income, it may face challenges in ensuring equal access to education for women.

Conclusion: Findings and Limitations

Findings:

We found that there is a strong correlation between education and HDI. To find this correlation, we grouped by continent. Because several continents appear either above or below the line of best fit, there are regional factors at play in the relationship between education and HDI. We also found that in order to understand literacy rate within a country, it is necessary to look at the regional context of that country. Thus, HDI - a measure of circumstances within a country - seems incommensurate with the task of understanding literacy rates in the world. Finally, even though HDI is considered to be a uniform measurement for development across the world, we found it does not address all aspects of development. As we discussed in the case for women empowerment, many countries with a high HDI score have a poor record in terms of indicators of women empowerment.

Limitations:

Even though there is a strong correlation between education and HDI, we cannot establish a causal relationship between the two variables. We would need to conduct further research to find out what, if any, actual causal relationship is at play. A more significant limitation of our analysis is the time frame captured by our data. Our HDI data only were from the year 2021. Worse, when it came to data on literacy by country, the years for most recent data were scattered. Evidently it is hard to collect reliable statistics on literacy rate. This meant that for many countries, the most recent literacy rate statistic was not, in fact, all that recent.

References

R Packages:

Tidyverse:

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Dplyr:

Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.0, https://CRAN.R-project.org/package=dplyr.

Sf:

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

Viridis:

Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.6.2.

Maps:

Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2022). maps: Draw Geographical Maps. R package version 3.4.1, https://CRAN.R-project.org/package=maps.

Gglpot2:

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Purrr:

Wickham H, Henry L (2023). purrr: Functional Programming Tools. R package version 1.0.1, https://CRAN.R-project.org/package=purrr.

Giscor:

Hernangomez D (2023). giscoR: Download Map Data from GISCO API - Eurostat. https://doi.org/10.5281/zenodo.4317946, https://ropengov.github.io/giscoR/

Heatmaply:

Tal Galili, Alan O’Callaghan, Jonathan Sidi, Carson Sievert; heatmaply: an R package for creating interactive cluster heatmaps for online publishing, Bioinformatics, , btx657, https://doi.org/10.1093/bioinformatics/btx657

Htmlwidgets:

Vaidyanathan R, Xie Y, Allaire J, Cheng J, Sievert C, Russell K (2023). htmlwidgets: HTML Widgets for R. R package version 1.6.1, https://CRAN.R-project.org/package=htmlwidgets.

Kableextra:

Zhu H (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4, https://CRAN.R-project.org/package=kableExtra.

Zoo:

Achim Zeileis and Gabor Grothendieck (2005). zoo: S3 Infrastructure for Regular and Irregular Time Series. Journal of Statistical Software, 14(6), 1-27. doi:10.18637/jss.v014.i06

Ggrepel:

Slowikowski K (2023). ggrepel: Automatically Position Non-Overlapping Text Labels with ‘ggplot2’. R package version 0.9.3, https://CRAN.R-project.org/package=ggrepel.

Broom:

Robinson D, Hayes A, Couch S (2023). broom: Convert Statistical Objects into Tidy Tibbles. R package version 1.0.3, https://CRAN.R-project.org/package=broom.

Ggally:

Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2021). GGally: Extension to ‘ggplot2’. R package version 2.1.2, https://CRAN.R-project.org/package=GGally.

Gapminder:

Bryan J (2023). gapminder: Data from Gapminder. R package version 1.0.0, https://CRAN.R-project.org/package=gapminder.

Data sets:

HDI data set:

United Nations, “HDR21-22_Statistical_Annex_HDI_table.xlsx”, 2023, .xlsx, https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

OECD Abbreviations:

OECD, “Migration”, .xls, https://www.oecd.org/migration/mig/34107835.xls

Unemployment:

World Bank, Unemployment, total (% of total labor force) (modeled ILO estimate)”, February 21, 2023, .csv, https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS

Unemployment, Women:

World Bank, “Unemployment, female (% of female labor force) (modeled ILO estimate)”, February 21, 2023, .csv, https://data.worldbank.org/indicator/SL.UEM.TOTL.FE.ZS

Literacy rate by country:

UNESCO institute for statistics, “Literacy Rate by Country 2023”, World Population Review.com, .csv, https://worldpopulationreview.com/country-rankings/literacy-rate-by-country

Literacy rate, World Bank:

UNESCO Institute for Statistics, “Literacy rate, adult total (% of people ages 15 and above)”, October 24, 2022, World Bank, .csv, https://data.worldbank.org/indicator/SE.ADT.LITR.ZS